code: def test_backtester_test_metrics(iclaims_training_data, metrics):
      df = iclaims_training_data
  
      lgt = LGT(
          response_col="claims",
          date_col="week",
          seasonality=1,
          verbose=False,
          estimator="stan-map",
      )
  
      backtester = BackTester(
          model=lgt,
          df=df,
          forecast_len=3,
          n_splits=1,
      )
  
      backtester.fit_predict()
      eval_out = backtester.score(metrics=metrics)
      evaluated_metrics = set(eval_out["metric_name"].tolist())
  
      if metrics is None:
          expected_metrics = [
              x.__name__ for x in backtester._default_metrics
          ]
      elif isinstance(metrics, list):
          expected_metrics = [x.__name__ for x in metrics]
      else:
          expected_metrics = [metrics.__name__]
  
      assert set(expected_metrics) == evaluated_metrics
  
file_path: orbit/tests/orbit/diagnostics/test_backtest.py
imports: import pytest
  import pandas
  import numpy
  from orbit.diagnostics.backtest import TimeSeriesSplitter, BackTester
  from orbit.diagnostics.metrics import smape, wmape, mae
  from orbit.models import LGT, KTRLite
label_desc: Use image node_id 3 for calling a global function with example usage: test_backtester_test_metrics(iclaims_training_data, metrics) without return types